Papers by Guangzhen Zhao
Keyphrase Generation via Soft and Hard Semantic Corrections (2022.emnlp-main)
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| Challenge: | Extensive experiments show that CorrKG is capable of generating high-quality keyphrases. |
| Approach: | They propose a correction model CorrKG on top of the MLE pipeline to correct the biases . the adaptive adaptive mass learning scheme is designed to better fit OT and FreqFS . |
| Outcome: | The proposed model overcomes the semantic biases in keyphrase generation using OT and FreqFS techniques. |
Seeking Rational Demonstrations for Large Language Models: A Domain Generalization Approach to Unsupervised Cross-Domain Keyphrase Generation (2025.acl-short)
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| Challenge: | Unsupervised cross-domain keyphrase generation is crucial in real-world natural language processing scenarios, but its accuracy is limited by the distribution shift between source and target domain. |
| Approach: | They propose to seek rational demonstrations from the source domain and to use them to improve their ability in the unsupervised cross-domain keyphrase generation setting. |
| Outcome: | The proposed model achieves state-of-the-art on widely used cross-domain KG benchmarks and the results are published in the journal Nature. |
Table-based Fact Verification with Self-labeled Keypoint Alignment (2022.coling-1)
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| Challenge: | Existing methods for fact verification rely on graph feature or data augmentation but fail to investigate evidence correlation between statement and table effectively. |
| Approach: | They propose a self-labeled keypoint alignment model to explore correlation between statement and table . they propose integrating a mixture-of experts block to integrate interacted information . |
| Outcome: | The proposed model outperforms the state-of-the-art models and captures interpretable evidence words on three widely-studied datasets. |
TrustTable: A Neuro-Symbolic Auditing Framework for Faithful Table QA (2026.acl-long)
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| Challenge: | Large Language Models (LLMs)-based TableQA models exhibit unfaithful behavior where correct answers are derived through erroneous reasoning paths. |
| Approach: | They propose a neuro-symbolic framework to audit LLM reasoning processes . it enforces factual grounding and ensures logical soundness by verifying reasoning chains . |
| Outcome: | The proposed framework outperforms LLM judges in majority voting and rejection sampling with process supervision. |